Aspect-based Sentiment Analysis of Scientific Reviews - Openreview dataset

The dataset contains all the data used in the JCDL 2020 research paper: Aspect-based Sentiment Analysis of Scientific Reviews

The dataset is split into multiple files containing all the sentence annotations and the ICLR open review dataset (with reviews and scores and the confidence scores, final recommendation, etc.) for the last three years.

The file "iclr_conf.p" is a pickle file which contains a NumPy array object.
The array contains 2681 rows corresponding to each accepted or rejected paper of 2017,2018,2019
Each row contains 4 columns.
The first column is the link of the paper in openreview.net, from where the data related to the paper is collected.
The second column is either 0 or 1, corresponding to the final decision: rejection or acceptance respectively.
The third column is the year of the conference for the particular submission.
The fourth column is another NumPy array containing 3 reviews in 3 rows. Each row of this array contains 3 columns containing the list of sentences in the same sequence as it appears in the text of the review, the confidence(ranging from 1-5), and the rating(ranging(1-10)) respectively.

Each line of the file "sentences.csv" contains one sentence whose corresponding annotation is provided in the corresponding line in the file "annotations.csv"
The file "annotations.csv" is a file containing 8 comma-separated integers in each line.
Each column corresponds to the following aspects: Appropriateness, Clarity, Originality, Empirical/Theoretical Soundness, Meaningful Comparison, Substance,
Impact of Dataset/Software/Ideas and Recommendation.
An integer 0,1,2,3 corresponds to the following sentiment labels of the sentence on that aspect: Absent, Positive, Negative, Neutral

Please cite our paper published in JCDL-2020 if you use our data: https://dl.acm.org/doi/10.1145/3383583.3398541